System, method and computer program for predictive maintenance

Abstract

To provide a method, system and computer program for predictive maintenance.SOLUTION: The method may include: receiving operation data associated with one or more injection systems, where the operation data includes one or more operation parameters associated with one or more operations of the one or more injection systems; determining one or more prediction scores for the one or more injection systems based on the operation data, where the one or more prediction scores include one or more predictions of one or more operation failures or misuses for the one or more injection systems; and providing maintenance data associated with the one or more operation failures or misuses, where the maintenance data is based on the one or more prediction scores.SELECTED DRAWING: Figure 1A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 62 / 547,300, filed August 18, 2017, the entire disclosure of which is incorporated herein by reference in its entirety.

[0002] This disclosure relates generally to systems, devices, products, apparatus, and methods used for predictive maintenance, and in one particular embodiment, to systems, products, and methods for predictive maintenance of injection systems. [Background technology]

[0003] The availability (e.g., uptime to perform operations, proper functioning and / or performance of operations, components, devices, functions, and / or non-failure of operations, etc.) and proper use (e.g., by a user or operator, etc.) of an injection system, for example as part of an imaging suite, can impact life-saving diagnosis and monitoring of medical treatment for a patient's illness or condition. If an injection system (e.g., one or more components or devices of an injection system, etc.) fails or is used improperly, imaging can be interrupted and / or a patient's medical procedure and / or treatment may be delayed and / or improperly performed. Thus, there is a need in the art to improve the availability and usage of injection systems (e.g., to reduce or prevent downtime, improper functioning, failures, and / or improper use by a user or operator of the injection system, etc.). Summary of the Invention [Means for solving the problem]

[0004] Thus, provided are systems, devices, products, apparatus, and / or methods for a maintenance prediction system that improves availability and / or usage of an injection system by predicting operational failure and / or misuse of the injection system before the injection system fails and / or is used improperly and by providing maintenance data related to the predicted operational failure and / or misuse. For example, conventional injection systems do not have a mechanism for predicting operational failure and / or misuse before the injection system (e.g., one or more components or devices of the injection system, one or more operations of the injection system, etc.) fails and / or is used improperly. As such, conventional injection systems may not provide for: (i) repairing, inspecting, updating, and / or replacing (e.g., scheduling and / or performing repairs, inspections, updates, and / or replacements, etc.) of an injection system (e.g., one or more components or devices of an injection system) before the injection system fails and / or is misused; providing maintenance data (e.g., maintenance actions, training information, etc.) to a user or operator of the injection system that the user or operator can perform to reduce the risk (e.g., probability, likelihood, etc.) of failure and / or misuse of the injection system; providing maintenance data (e.g., information regarding the correct components and / or devices (e.g., replacement parts, maintenance tools, etc.) and / or operation (e.g., inspection records, error codes, inspection procedures, etc.)) to a service technician and / or user or operator of the injection system to repair, inspect, update, and / or replace the injection system; (iv) automatically ensuring regulatory compliance of the injection system before the injection system becomes non-compliant with one or more regulations; (v) inspection benchmarking of an inventory system, and / or the like.

[0005] According to non-limiting embodiments or aspects, a computer-implemented method for predictive maintenance is provided, the computer-implemented method including the steps of receiving, by a computer system having one or more processors, operational data associated with one or more infusion systems, the operational data including one or more operational parameters associated with one or more operations of the one or more infusion systems; determining, by the computer system, one or more predictive scores for the one or more infusion systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuse for the one or more infusion systems; and providing, by the computer system, maintenance data associated with the one or more operational faults or misuse, the maintenance data being based on the one or more predictive scores.

[0006] According to non-limiting embodiments or aspects, a predictive maintenance system is provided comprising a computer system comprising one or more processors, the one or more processors programmed or configured to: receive operational data associated with one or more injection systems, the operational data including one or more operational parameters associated with one or more operations of the one or more injection systems, determine one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuses for the one or more injection systems, and provide maintenance data related to the one or more operational faults or misuses, the maintenance data being based on the one or more predictive scores.

[0007] According to non-limiting embodiments or aspects, a computer program product for predictive maintenance is provided, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to receive operational data associated with one or more infusion systems, the operational data including one or more operational parameters associated with one or more operations of the one or more infusion systems, determine one or more predictive scores for the one or more infusion systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuses for the one or more infusion systems, and provide maintenance data related to the one or more operational faults or misuses, the maintenance data being based on the one or more predictive scores.

[0008] In some non-limiting embodiments or aspects, the maintenance data includes a prompt to prompt a user to initiate at least one maintenance action associated with the one or more infusion systems.

[0009] In some non-limiting embodiments or aspects, the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner as indicated by the maintenance data, or any combination thereof.

[0010] In some non-limiting embodiments or aspects, the maintenance data includes instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

[0011] In some non-limiting embodiments or aspects, the at least one maintenance action includes at least one of scheduling an inspection for the one or more injection systems, performing a particular operation with the one or more injection systems, or any combination thereof.

[0012] In some non-limiting embodiments or aspects, the one or more operating parameters include a cleanliness rating related to the cleanliness of the one or more injection systems.

[0013] In some non-limiting embodiments or aspects, the method further includes determining, by the computer system, a cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0014] In some non-limiting embodiments or aspects, the one or more processors are further programmed or configured to determine a cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0015] In some non-limiting embodiments or aspects, the instructions further cause the at least one processor to determine a cleanliness rating based on at least one of one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0016] In some non-limiting embodiments or aspects, the one or more operational faults or misuses of the one or more infusion systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more infusion systems a user input from a user of the one or more infusion systems that causes the one or more infusion systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

[0017] Further non-limiting embodiments or aspects are described in the following numbered paragraphs.

[0018] Item 1. A computer-implemented method for predictive maintenance, the computer-implemented method including: receiving, by a computer system having one or more processors, operational data associated with one or more injection systems, the operational data including one or more operational parameters associated with one or more operations of the one or more injection systems; determining, by the computer system, one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuse for the one or more injection systems; and providing, by the computer system, maintenance data associated with the one or more operational faults or misuse, the maintenance data based on the one or more predictive scores.

[0019] Clause 2. The computer-implemented method of clause 1, wherein the maintenance data includes a prompt prompting a user to initiate at least one maintenance action associated with the one or more injection systems.

[0020] Clause 3. The computer-implemented method of clause 1 or 2, wherein the at least one maintenance action includes at least one of: scheduling an inspection for one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

[0021] Clause 4. The computer-implemented method of any one of clauses 1 to 3, wherein the maintenance data includes instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

[0022] Clause 5. The computer-implemented method of any one of clauses 1 to 4, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, performing a particular operation by the one or more injection systems, or any combination thereof.

[0023] Clause 6. The computer implemented method of any one of clauses 1 to 5, wherein the one or more operational parameters include a cleanliness rating related to the cleanliness of the one or more injection systems, the method further comprising determining, by the computer system, the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0024] Clause 7. The computer-implemented method of any one of clauses 1 to 6, wherein the one or more operational failures or misuses of the one or more injection systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more injection systems a user input from a user of the one or more injection systems that causes the one or more injection systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

[0025] Item 8. A predictive maintenance system comprising: a computer system having one or more processors programmed or configured to: receive operational data associated with one or more injection systems, the operational data including one or more operational parameters associated with one or more operations of the one or more injection systems; determine one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuses for the one or more injection systems; and provide maintenance data related to the one or more operational faults or misuses, the maintenance data being based on the one or more predictive scores.

[0026] Clause 9. The system of clause 8, wherein the maintenance data includes a prompt prompting a user to initiate at least one maintenance action associated with one or more injection systems.

[0027] Clause 10. The system of clause 8 or 9, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

[0028] Clause 11. The system of any one of clauses 8 to 10, wherein the maintenance data includes instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

[0029] Clause 12. The system of any one of clauses 8 to 11, wherein the at least one maintenance action includes at least one of: scheduling an inspection for one or more injection systems, performing a particular operation by one or more injection systems, or any combination thereof.

[0030] Clause 13. The system of any one of clauses 8 to 12, wherein the one or more operational parameters include a cleanliness rating related to the cleanliness of the one or more injection systems, and the one or more processors are further programmed or configured to determine the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0031] Clause 14. The system of any one of clauses 8 to 13, wherein the one or more operational failures or misuses of the one or more injection systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more injection systems a user input from a user of the one or more injection systems that causes the one or more injection systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

[0032] Item 15. A computer program product for predictive maintenance, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to receive operational data associated with one or more injection systems, the operational data including one or more operational parameters associated with one or more operations of the one or more injection systems, determine one or more predictive scores for the one or more injection systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational faults or misuses for the one or more injection systems, and provide maintenance data related to the one or more operational faults or misuses, the maintenance data being based on the one or more predictive scores.

[0033] Clause 16. The computer program product of clause 15, wherein the maintenance data includes a prompt prompting a user to initiate at least one maintenance action associated with the one or more infusion systems.

[0034] Clause 17. The computer program product of clause 15 or 16, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

[0035] Clause 18. The computer program product of any one of clauses 15 to 17, wherein the maintenance data includes instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

[0036] Clause 19. The computer program product of any one of clauses 15 to 18, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, performing a particular operation by the one or more injection systems, or any combination thereof.

[0037] Clause 20. The computer program product of any one of clauses 15 to 19, wherein the one or more operational parameters include a cleanliness rating related to the cleanliness of the one or more injection systems, and the instructions cause the at least one processor to determine the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof.

[0038] Clause 21. The computer program product of any one of clauses 15 to 20, wherein the one or more operational failures or misuses of the one or more infusion systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, or receiving by the one or more infusion systems a user input from a user of the one or more infusion systems that causes the one or more infusion systems to operate contrary to one or more predetermined operational thresholds.

[0039] These and other features and characteristics of the present invention, as well as the method of operation and function of the combination of associated elements and parts of structure, and economy of manufacture, will become more apparent from a consideration of the following description and appended claims in conjunction with the accompanying drawings, all of which form a part of this specification, in which like reference numerals indicate corresponding parts in the various views. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0040] Additional advantages and details of the present invention are explained in more detail below in connection with exemplary embodiments or aspects illustrated in the accompanying schematic drawings. [Brief description of the drawings]

[0041] [Figure 1A]FIG. 1 illustrates a non-limiting embodiment or aspect of an environment in which the systems, devices, products, apparatus, and / or methods described herein may be implemented in accordance with the principles of the invention. [Figure 1B] FIG. 1B is a diagram of a non-limiting embodiment or aspect of the injection system shown in FIG. 1A. [Figure 1C] FIG. 1B is a diagram of a non-limiting embodiment or aspect of the injection system shown in FIG. 1A. [Diagram 2] FIG. 1C is a diagram of a non-limiting embodiment or aspect of a component of one or more of the systems or one or more devices of FIGS. 1A-1C. [Diagram 3] 1 is a flowchart of a non-limiting embodiment or aspect of a process for predictive maintenance. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0042] For purposes of the following description, the terms "end", "upper", "lower", "right", "left", "vertical", "horizontal", "top", "bottom", "transverse", "vertical" and their derivatives are intended to refer to the invention as it is oriented in the drawings. However, it should be understood that the invention may assume various alternative variations and step sequences unless expressly stated to the contrary. It should also be understood that the specific devices and processes illustrated in the accompanying drawings and described in the following specification are merely exemplary embodiments or aspects of the invention. Thus, specific dimensions and other physical characteristics associated with the embodiments or aspects disclosed herein are not to be considered limiting unless otherwise indicated.

[0043] As used herein, aspects, components, elements, structures, acts, steps, functions, instructions, and / or the like should not be construed as critical or essential unless expressly described as such. Additionally, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more" and "at least one." Additionally, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with "one or more" or "at least one." When only one item is intended, the term "a" or similar language is used. Additionally, as used herein, terms such as "has," "have," "having," and the like are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless otherwise specified.

[0044] The terms "communication" and "communicating" as used herein may refer to receiving, accepting, sending, forwarding, providing, etc., information (e.g., data, signals, messages, instructions, commands, and / or the like). A unit (e.g., a device, a system, a component of a device or system, combinations thereof, and / or the like) communicating with another unit means that the unit can directly or indirectly receive information from the other unit and / or transmit information to the other unit. This may refer to a direct or indirect connection that is wired and / or wireless in nature. Furthermore, two units may be in communication with each other even if the information transmitted is modified, processed, relayed, and / or routed between the first unit and the second unit. For example, a first unit may be in communication with a second unit even if the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediate unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments or aspects, a message may refer to a network packet (e.g., a data packet and / or the like) that includes data. It may be understood that many other configurations are possible. It may be understood that many other configurations are possible.

[0045] The term "server" as used herein may refer to one or more computing devices, such as a processor, storage device, and / or similar computing components that communicate with client devices and / or other computing devices over a network, such as the Internet or a private network, and in some instances may facilitate communication between other servers and / or client devices. It may be understood that various other configurations may be envisioned. The term "system" as used herein may refer to one or more computing devices or combinations of computing devices, such as, but not limited to, a processor, a server, a client device, a software application, and / or other similar components. In addition, references to a "server" or a "processor" as used herein may refer to an already listed server and / or processor, a different server and / or processor, and / or a combination of servers and / or processors that are listed to perform a previous step or function. For example, as used in the specification and claims, a first server and / or a first processor listed to perform a first step or function may refer to the same or a different server and / or processor listed to perform a second step or function.

[0046] Non-limiting embodiments or aspects of the present invention relate to systems, devices, products, apparatus, and / or methods for a maintenance prediction system that improves availability and / or usage of an infusion system by predicting operational failure and / or misuse of the infusion system before the infusion system fails and / or is used improperly and by providing maintenance data related to the predicted operational failure and / or misuse.

[0047] In this manner, embodiments or aspects of the present invention may provide for: (I) repairing, servicing, updating, and / or replacing (e.g., scheduling and / or performing repairs, servicing, updating, and / or replacement) an infusion system (e.g., one or more components or devices of an infusion system, one or more operations of an infusion system, etc.) before the infusion system (e.g., one or more components or devices of an infusion system, one or more operations of an infusion system, etc.) fails and / or is misused, thereby (a) reducing or preventing downtime of the infusion system; (b) extending the life of the infusion system (e.g., reducing the time to failure necessitating replacement of the infusion system, etc.); (c) increasing the number and / or likelihood of successful treatments and / or therapies for a patient; and (d) increasing the efficiency of scheduling and / or performing repairs, servicing, updating, and / or replacement (e.g., automatically scheduling and / or performing repairs, servicing, updating, and / or replacement on the infusion system, automatically prompting a user to schedule and / or perform repairs, servicing, updating, and / or replacement on the infusion system, etc.). (II) providing a user or operator of the injection system with maintenance data (e.g., maintenance actions, training information, etc.) that the user or operator can perform to reduce the risk (e.g., probability, likelihood, etc.) of failure and / or misuse of the injection system, thereby (a) reducing or preventing continued misuse of the injection system, (b) improving image quality and / or patient care (e.g., reducing the occurrence of repeat injections and / or scans, etc.), (c) reducing contrast waste, and / or the like; (III) providing maintenance data (e.g., information regarding components and / or devices (e.g., replacement parts, inspection tools, etc.) and / or operation (e.g., inspection records, error codes, inspection procedures, etc.)) to a service technician and / or user or operator of the injection system to repair, inspect, update, and / or replace the injection system, thereby enabling the service technician to (a) have the correct parts, tools, and / or information for a particular repair, inspection, update, and / or replacement; (b) performing multiple repairs, inspections, updates, and / or replacements;(IV) automatically ensuring regulatory compliance of an infusion system before the infusion system becomes non-compliant with one or more regulations, thereby (a) ensuring calibration settings of the infusion system, (b) reducing patient infections, (c) increasing the cleanliness of the infusion system, and / or the like; (V) providing inspection criteria for an inventory system, thereby (a) providing information regarding warranty forecasting, inspection inventory planning, inspection resource planning, etc., that a user or customer can use to improve their knowledge base for preventive maintenance, (b) defining use cases for future products, (c) improving training focus areas, and / or the like;

[0048] Reference is now made to Figure 1A, which is a diagram of an example environment 100 in which the devices, systems, and / or methods described herein may be implemented. As shown in Figure 1A, the environment 100 includes a maintenance prediction system 102, an injection system 104, a remote system 106, and / or a network 108. The maintenance prediction system 102, the injection system 104, and / or the remote system 106 may be interconnected (e.g., establish a connection to communicate) via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0049] In some non-limiting embodiments or aspects, the maintenance prediction system 102 includes one or more devices that can receive data and / or information (e.g., operational data, maintenance data, etc.) from the infusion system 104 and / or the remote system 106 via the network 108 and / or communicate data and / or information (e.g., operational data, maintenance data, etc.) to the infusion system 104 and / or the remote system 106 via the network 108. For example, the maintenance prediction system 102 can include computing devices such as one or more computers, portable computers (e.g., tablet computers), mobile devices (e.g., cell phones, smartphones, watches, glasses, lenses, and / or wearable devices such as clothing, PDAs, and / or the like), servers (e.g., transaction processing servers), groups of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the maintenance prediction system 102 communicates with data storage devices that may be local or remote to the maintenance prediction system 102. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can receive data and / or information from a data storage device, store data and / or information in a data storage device, communicate data and / or information to a data storage device, or retrieve data and / or information (e.g., operational data, maintenance data, etc.) stored in a data storage device. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can be implemented within the infusion system 104 and / or within the remote system 106.

[0050] In some non-limiting embodiments or aspects, the infusion system 104 includes one or more devices that can receive data and / or information (e.g., operational data, maintenance data, etc.) from the maintenance prediction system 102 and / or the remote system 106 via the network 108 and / or communicate data and / or information (e.g., operational data, maintenance data, etc.) to the maintenance prediction system 102 and / or the remote system 106 via the network 108. For example, the infusion system 104 can include computing devices such as one or more computers, portable computers (e.g., tablet computers), mobile devices (e.g., cell phones, smartphones, watches, glasses, lenses, and / or wearable devices such as clothing, PDAs, and / or the like), servers (e.g., transaction processing servers), groups of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the infusion system 104 includes one or more devices (e.g., one or more sensors (e.g., flow sensors, temperature sensors, accelerometers, vibration monitoring sensors, strain gauges, motor current sensors, optical sensors (e.g., barcode scanners, etc.), image sensors (e.g., digital cameras, etc.), one or more input components, one or more communication interfaces, etc.) that can receive, determine, measure, and / or sense operational data associated with the infusion system 104. For example, the operational data can include one or more operational parameters associated with one or more operations of the infusion system 104, and the infusion system 104 can receive, determine, measure, and / or sense the one or more operational parameters. In some non-limiting embodiments or aspects, the infusion system 104 communicates with a data storage device, which may be local or remote to the infusion system 104. In some non-limiting embodiments or aspects, the infusion system 104 can receive data and / or information from a data storage device, store data and / or information in a data storage device, communicate data and / or information to a data storage device, or retrieve data and / or information (e.g., operational data, maintenance data, etc.) stored in a data storage device.

[0051] In some non-limiting embodiments or aspects, the infusion system 104 includes multiple infusion systems 104. For example, the multiple infusion systems 104 can receive data and / or information (e.g., operational data, maintenance data, etc.) from and / or communicate data and / or information (e.g., operational data, maintenance data, etc.) with the maintenance prediction system 102, the remote systems 106, and / or each other via the network 108. In some non-limiting embodiments or aspects, one or more of the infusion systems of the multiple infusion systems 104 can be different from one or more other infusion systems of the multiple infusion systems 104 (e.g., different types of infusion systems, such as systems from different manufacturers, different models of infusion systems, different versions of the same model of infusion system, infusion systems for different types of infusions (e.g., CT-based injection, MRI-based injection, etc.), and / or the like). In some non-limiting embodiments or aspects, one or more of the injection systems of the plurality of injection systems 104 may be identical to one or more other injection systems of the plurality of injection systems 104 (e.g., injection systems of the same type, such as systems from the same manufacturer, injection systems of the same model, injection systems of the same version, injection systems for the same type of injection (e.g., CT-based injection, MRI-based injection, etc.), and / or the like). In some non-limiting embodiments or aspects, one or more of the injection systems of the plurality of injection systems 104 may be separate from one or more other injection systems of the plurality of injection systems 104 (e.g., separate injection systems at the same location, separate injection systems at the same imaging site, separate injection systems at different remote locations, separate injection systems at different imaging sites, etc.).

[0052] In some non-limiting embodiments or aspects, the injection system 104 is configured to inject, deliver, or administer a contrast fluid containing a contrast agent to the patient, and in some non-limiting embodiments or aspects, the injection system 104 is further configured to inject or administer saline or other fluids to the patient before, during, or after administration of the contrast fluid. For example, the injection system 104 can inject one or more prescribed doses of contrast fluid directly into the patient's bloodstream via a hypodermic needle and syringe. In some non-limiting embodiments or aspects, the injection system 104 can be configured to continuously administer saline to the patient through a peripheral IV line (PIV) and catheter, and one or more prescribed doses of contrast fluid can be introduced into the PIV and administered to the patient via the catheter. In some non-limiting embodiments or aspects, the injection system 104 is configured to administer a specific amount of saline after injecting a predetermined dose of contrast fluid.

[0053] In some non-limiting embodiments or aspects, the injection system 104 is configured to administer a single contrast agent. In some non-limiting embodiments or aspects, the injection system 104 is configured to deliver two or more different contrast agents. In implementations where the injection system 104 is configured to deliver multiple contrast agents, the injection system may allow an operator to switch between configurations depending on the intended procedure. The amount of each contrast agent delivered by the injection system 104 may vary based on the injection protocol being used. For example, a particular injection protocol may be used to achieve a desired blood, plasma, and / or tissue level of the contrast agent. A physician or other qualified medical personnel (and / or the injection system 104) may determine an appropriate injection protocol according to which the contrast agent is delivered to a particular patient using patient-related metrics (e.g., age, weight, height, body mass index (BMI), cardiac output, type of procedure to be performed, etc.). The injection system 104 may be configured to inject two or more contrast agents individually, sequentially, or simultaneously. Thus, in some non-limiting embodiments or aspects, the injection system 104 can include two or more reservoirs, such as vials or syringes, that can hold a radiopharmaceutical prior to administration. The injection system 104 can further include additional medical fluid reservoirs that can hold, for example, saline, other drugs, or other fluids.

[0054] In some non-limiting embodiments or aspects, the infusion system 104 may be configured as described in U.S. Patent Application No. 09 / 267,238, filed March 12, 1999, which issued as U.S. Patent No. 6,317,623; U.S. Patent Application No. 09 / 715,330, filed November 17, 2000, which issued as U.S. Patent No. 6,643,537; U.S. Patent Application No. 09 / 982, filed October 18, 2001, which issued as U.S. Patent No. 7,094,216; No. 10 / 326,582, filed Dec. 20, 2002, which issued as U.S. Patent No. 7,549,977; U.S. Patent Application No. 10 / 825,866, filed Apr. 16, 2004, which issued as U.S. Patent No. 7,556,619; U.S. Patent Application No. 12 / 437,011, filed May 7, 2009, which issued as U.S. Patent No. 8,337,456; U.S. Patent Application No. 8,147,464, filed Dec. 20, 2003, which issued as U.S. Patent No. 7,549,977; U.S. Patent Application No. 8,147,464, filed Dec. 20, 2003, which issued as U.S. Patent No. 7,556,619; No. 12 / 476,513, filed June 2, 2009, which issued as U.S. Pat. No. 8,540,698; U.S. Pat. No. 11 / 004,670, filed December 3, 2004, which issued as U.S. Pat. No. 8,540,698; U.S. Pat. No. 14 / 826,602, filed August 14, 2015, which issued as U.S. Pat. No. 9,463,335; U.S. Pat. No. 14 / 826,602, filed August 11, 2016, which issued as International Application No. PCT / US2016 / 046587; The present invention may include one or more exemplary injection systems, fluid delivery systems, and / or injectors disclosed in International Patent Application Publication No. WO2017 / 027724A1, published February 16, 2017, and International Patent Application Publication No. WO2017 / 040152A1, filed as International Application No. PCT / US2016 / 048441 on August 24, 2016, and published March 9, 2017, the disclosures of each of which are incorporated herein by reference in their entirety.In some non-limiting embodiments or aspects, the injection system 104 includes a MEDRAD® Stellant CT Injection System with a Certegra® Workstation provided by Bayer and / or a MEDRAD® MRXperion MR Injection System with a Radimetrics™ Enterprise Platform also provided by Bayer.

[0055] In some non-limiting embodiments or aspects, the infusion system 104 includes one or more flow sensors for directly measuring the flow rate and / or volume of the fluid stream. For example, with reference to FIG. 1B, the infusion system 104 can include an injector 120 configured to provide one or more fluids from one or more fluid sources (e.g., Fluid 1, Fluid 2, Fluid N, etc.) to a flow tube 122 for delivery to a medical device 128 (e.g., a catheter, etc.) according to an injection protocol (e.g., according to one or more injection parameters, etc.). As an example, the infusion system 104 can include a flow sensor 124 (e.g., an ultrasonic mass flow sensor, such as those manufactured by Transonic Systems, Inc., etc.) configured to measure the flow rate and / or volume of the fluid stream. In such an example, the flow sensor 124 can be configured to directly measure the flow rate of the fluid through the flow tube 122 and / or the volume of the fluid through the flow tube 122 (e.g., the total volume delivered for injection, etc.). The flow sensor 124 may measure the flow rate and / or volume of a fluid flow in the flow tube 122 controlled and / or supplied by an injector 120 (e.g., a pump powered by a motor, etc.), such as a positive displacement pump, a non-positive displacement pump, a semi-positive displacement pump, a reciprocating pump, a piston pump, a vane pump, a flexible member pump, a lobe pump, a gear pump, a circumferential piston pump, a screw pump, a centrifugal pump, a turbine pump, an impeller pump, and / or the like. For example, the flow sensor 124 may be attached or mounted (e.g., via a clip, adhesive, etc.) to an exterior surface of the flow tube 122.

[0056] 1B , in some non-limiting embodiments or aspects, the flow sensor 124 provides a real-time feedback signal via a feedback control loop between the flow sensor 124 and the injector 120. For example, the real-time feedback signal may include a real-time measurement of the flow rate and / or volume of the fluid flow in the flow tube 122. By way of example, the injector 120 may be programmed or configured to control the injection protocol (e.g., stop the injection, adjust injection control parameters, control fluid delivery from one or more fluid sources to the flow tube 122, etc.) based on the real-time feedback signal from the flow sensor 124. In some non-limiting embodiments or aspects, the injection system 104 (e.g., the flow sensor 124, etc.) may be adjusted or calibrated (e.g., may include one or more operating parameters, etc.) for more accurate measurement of the flow rate and / or volume of the fluid in a particular flow tube 122 (e.g., a particular disposable tubing set, etc.).

[0057] In some non-limiting embodiments or aspects, the injection system 104 includes an air sensor 126 configured to detect air or gas in the fluid flow. For example, the air sensor 126 can be configured to directly measure the amount of air or gas in the fluid flowing through the flow tube 122. In some non-limiting embodiments or aspects, the air sensor 126 provides a real-time feedback signal via a feedback control loop between the air sensor 126 and the injector 120. For example, the real-time feedback signal may include a real-time measurement of the amount of gas in the fluid flowing through the flow tube 122. As an example, the injector 120 can be programmed or configured to control an injection protocol (e.g., adjust injection control parameters, control fluid delivery to the flow tube 122, etc.) based on the real-time feedback signal from the air sensor 126.

[0058] 1B is shown with respect to a single injector 120 (e.g., a single pump) controlling fluid delivery from multiple fluid sources 1, 2, ... N, etc. to a single flow tube 122 with a single flow sensor 124 (and / or a single air sensor 126) providing a single real-time feedback signal to the single injector 120, non-limiting embodiments or aspects are not so limited and the injector system 104 may include respective injectors 120 (and / or one or more respective control valves, etc.) controlling fluid delivery to a respective flow tube 122 for each respective fluid source 1, 2, ... N, etc. with a respective flow sensor 124 (and / or a respective air sensor 126) providing a respective real-time feedback signal to the respective injector 120. For example, each of the respective flow tubes 122 may be combined after a respective flow sensor 124 to deliver a combined fluid flow from each of the respective fluid sources 1, 2, ... N to a medical device 128.

[0059] In some non-limiting embodiments or aspects, the injection system 104 includes one or more sensors for measuring one or more operating parameters related to the cleanliness of the injection system 104. For example, dirt and / or fluid (e.g., contrast medium, etc.) may be undesirably transferred to one or more components or devices of the injection system 104 by a user or operator's hands and / or one or more drops or leaks. As an example, referring to FIG. 1C, the injector 120 may be contained within a housing 150 and may include one or more ports 152 (e.g., syringe ports, etc.) for connecting to a proximal end of one or more fluid sources 1, 2, ... N (e.g., syringes 154 containing fluid 1, fluid 2, etc.) and for connecting plungers 156 to respective piston elements. The syringe ports 152 are generally located at one end of the housing 150, as shown, for example, in FIG. 1C. In some non-limiting embodiments or aspects, the syringe 154 may include at least one bar code (BC) that includes information regarding the syringe's dimensions, volume, pressure tolerance, and / or information regarding the fluid contained within the syringe 154. The at least one bar code (BC) may be read by an optical sensor 158 located on or recessed in at least a portion of the end of the housing 150 or an inner surface of at least one syringe port 154 of the injector 120.

[0060] In some non-limiting embodiments or aspects, the injection system 104 determines a cleanliness rating of the injection system 104 and / or the optical sensor 158 based on one or more scans of the at least one barcode (BC) by the optical sensor 158. For example, the injection system 104 can determine a numerical cleanliness rating based on a number of scans performed before a successful scan of the at least one barcode (BC) (e.g., more scans performed before a successful scan may indicate a lower cleanliness rating, a lower cleanliness rating, etc.) and / or based on a percentage of the optical sensor 158 field of view that is obscured (e.g., a field of view with a higher percentage that is obscured may indicate a lower cleanliness rating, a lower cleanliness rating by the percentage that is obscured, etc.). In some non-limiting embodiments or aspects, the injection system 104 can determine a wear rating of the injection system 104 and / or the optical sensor 158 in response to a maintenance action indicating that the optical sensor 158 has been cleaned. For example, the injection system 104 may determine that the cause of the cleanliness rating is a more permanent operational impairment (e.g., a scratch on the lens of the optical sensor 158) than a less permanent operational impairment (e.g., dirt or fluid on the lens of the optical sensor 158) that can be repaired or removed by a cleaning action.

[0061] 1B and 1C, the injection system 104 can include an image capture device 140, such as a digital camera and / or the like, positioned to have a field of view that includes one or more components or devices of the injection system 104 for capturing one or more images of one or more components or devices of the injection system 104. For example, with reference to FIG. 1B, the injection system 104 can include an image capture device 140 positioned to have a field of view (F) that includes each of the respective fluid sources 1, 2, ... N, the injector 120, the flow tube 122, and / or the medical instrument 128. 1C , the injection system 104 may include an image capture device 140 positioned to have a first field of view (F1) that includes each of the housing 150, the syringe 154, and / or the medical device 128, and / or a second field of view (F2) that includes one or more particular components or component connections of the injection system 104 (e.g., the port 152, the connection between the port 152 and the syringe 154, etc.). In such an example, one or more images of the field of view may be acquired by the image capture device 140, and the injection system 104 may analyze the acquired one or more images using image processing techniques, such as pattern recognition algorithms and / or the like, to identify a percentage of the injection system 104 (or one or more components thereof) that is riddled with undesirable contaminants (e.g., dirt, spilled contrast agent, etc.) and / or to identify leaks or cracks in one or more components or component connections of the injection system 104. As an example, the injection system 104 may use Insight Explorer image processing software available from Cognex Corporation of Natick, Massachusetts, and the image capture device 140 may be a DataMan 100 camera available from Cognex Corporation to identify a percentage of the injection system 104 that is contaminated with undesirable contaminants and / or to identify leaks or cracks in one or more components or component connections of the injection system 104.In such examples, the injection system 104 can compare features of the current image to training images or previous images to identify new or increased contaminant coverage and / or leaks or cracks in one or more components or component connections of the injection system 104.

[0062] In some non-limiting embodiments or aspects, image capture device 140 may be further configured to read at least one bar code (BC). For example, referring again to FIG. 1B, fluid sources 1, 2, ... N may include at least one bar code (BC) that includes information regarding the fluid source's dimensions, volume, pressure tolerances, and / or information regarding the fluid contained in fluid sources 1, 2, ... N. The at least one bar code (BC) may be read by image capture device 140, for example, before, during, and / or after connecting fluid sources 1, 2, ... N to injector 120. In such examples, the injection system 104 may determine a numerical cleanliness rating based on the number of scans performed before a successful scan of at least one barcode (BC) (e.g., more scans performed before a successful scan may indicate a lower cleanliness rating, a lower cleanliness rating, etc.) and / or based on the percentage of the image capture device 140 field of view that is obscured (e.g., a field of view with a higher percentage that is obscured may indicate a lower cleanliness rating, a lower cleanliness rating by the percentage that is obscured, etc.).

[0063] In some non-limiting embodiments or aspects, referring to FIG. 1C , the injection system 104 includes a force sensor 160 (e.g., a motor current sensor, a strain gauge, etc.) in the injector 120 configured to measure the force associated with moving the plunger 156 to expel fluid from the syringe 154. For example, a buildup of contrast agent and / or dirt in components of the injector 120 may increase the force required to move the plunger 156 to expel fluid from the syringe 154. As an example, the injection system 104 may determine a numerical cleanliness rating based on the force measured by the force sensor 160 required to move the plunger 156 to expel fluid from the syringe 154 for one or more injections (e.g., a greater measured force may indicate a lower cleanliness rating, may decrease the cleanliness rating, etc.). In such an example, the injection system 104 may compare a force measurement of a current or recent injection to a force measurement of a previous injection or a calibration measurement to determine a change in the numerical cleanliness rating as a percentage difference in the force measurements.

[0064] 1B , in some non-limiting embodiments or aspects, the injection system 104 includes a force sensor 160 (e.g., a motor current sensor, a strain gauge, etc.) in the injector 120 configured to measure the force associated with pumping fluid from the fluid sources 1, 2, ... N using the injector 120. For example, a buildup of contrast agent and / or dirt in components of the injector 120 increases the force required to pump fluid from the fluid sources 1, 2, ... N using the injector 120. As an example, the injection system 104 can determine a numerical cleanliness rating based on the force measured by the force sensor 160 required to deliver fluid from the fluid sources 1, 2, ... N using the injector 120 to the medical device 128 for one or more injections (e.g., a greater force measured may indicate a lower cleanliness rating, may decrease the cleanliness rating, etc.). In such an example, the injection system 104 can compare the force measurements of a current or recent injection to the force measurements of a previous injection or to a calibration measurement and determine a change in the numerical cleanliness rating as the percentage difference in the force measurements.

[0065] In some non-limiting embodiments or aspects, the remote system 106 may include one or more devices that can receive data and / or information (e.g., operational data, maintenance data, etc.) from the maintenance prediction system 102 and / or the infusion system 104 via the network 108 and / or communicate data and / or information (e.g., operational data, maintenance data, etc.) to the maintenance prediction system 102 and / or the infusion system 104 via the network 108. For example, the remote system 106 may include a computing device, such as a server, a group of servers, and / or other similar devices. In some non-limiting embodiments or aspects, the remote system 106 may be implemented by or instead of an original equipment manufacturer OEM of the infusion system 104 (e.g., an OEM of one or more components or devices of the infusion system 104), a provider of the infusion system 104, an imaging site or hospital that includes the infusion system 104, a service technician assigned to the infusion system 104, and / or the like.

[0066] Network 108 may include one or more wired and / or wireless networks. For example, network 108 may include a cellular network (e.g., a Long Term Evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad-hoc network, an intranet, the Internet, an optical fiber-based network, a cloud computing network, a short-range wireless communication network (e.g., a Bluetooth network, a near field communication (NFC) network, etc.), and / or the like, and / or combinations of these or other types of networks.

[0067] The number and arrangement of the systems, devices, and networks shown in Figures 1A-1C are provided as an example. There may be additional systems, devices, and / or networks to those shown in Figures 1A-1C, fewer systems, devices, and / or networks than those shown in Figures 1A-1C, different systems, devices, and / or networks than those shown in Figures 1A-1C, or systems, devices, and / or networks arranged differently than those shown in Figures 1A-1C. Furthermore, two or more systems or devices shown in Figures 1A-1C may be implemented within a single system or device, or a single system or device shown in Figures 1A-1C may be implemented as multiple distributed systems or devices. Additionally or alternatively, a set of systems or devices (e.g., one or more systems, one or more devices, etc.) of environment 100 may perform one or more functions described as being performed by other sets of systems or devices of environment 100.

[0068] Reference is now made to Figure 2, which is a diagram of example components of a device 200. The device 200 may correspond to one or more devices and / or systems of the maintenance prediction system 102, one or more devices and / or systems of the infusion system 104, and / or one or more devices and / or systems of the remote system 106. In some non-limiting embodiments or aspects, the maintenance prediction system 102, the infusion system 104, and / or the remote system 106 may include at least one device 200 and / or at least one component of the device 200. As shown in Figure 2, the device 200 may include a bus 202, a processor 204, a memory 206, a storage component 208, an input component 210, an output component 212, and a communication interface 214.

[0069] The bus 202 may include components that enable communication between components of the device 200. In some non-limiting embodiments or aspects, the processor 204 may be implemented in hardware, firmware, or a combination of hardware and software. For example, the processor 204 may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component that can be programmed to perform a function (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.). The memory 206 may include random access memory (RAM), read only memory (ROM), and / or other types of dynamic or static storage devices (e.g., flash memory, magnetic memory, optical memory, etc.) that store information and / or instructions for use by the processor 204.

[0070] The storage component 208 may store information and / or software related to the operation and use of the device 200. For example, the storage component 208 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, a solid-state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or other type of computer-readable medium along with a corresponding drive.

[0071] Input components 210 may include components that enable device 200 to receive information, such as by user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally or alternatively, input components 210 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output components 212 may include components that provide output information from device 200 (e.g., a display, a speaker, one or more light emitting diodes (LEDs), etc.).

[0072] The communication interface 214 may include transceiver-like components (e.g., a transceiver, a separate receiver and transmitter, etc.) that enable the device 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 214 may enable the device 200 to receive information from other devices and / or provide information to other devices. For example, the communication interface 214 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, and / or the like.

[0073] Device 200 may perform one or more processes described herein. Device 200 may perform these processes based on processor 204 executing software instructions stored by a computer-readable medium, such as memory 206 and / or storage component 208. Computer-readable media (e.g., non-transitory computer-readable media) are defined herein as non-transitory memory devices. A memory device includes memory space located within a single physical storage device or memory space spanning multiple physical storage devices.

[0074] Software instructions may be loaded into memory 206 and / or storage component 208 from other computer-readable media or from other devices via communications interface 214. The software instructions stored in memory 206 and / or storage component 208, when executed, may cause processor 204 to perform one or more of the processes described herein. Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more of the processes described herein. Thus, the embodiments or aspects described herein are not limited to any specific combination of hardware circuitry and software.

[0075] The number and arrangement of components shown in Figure 2 are provided as an example. In some non-limiting embodiments or aspects, device 200 may include additional components, fewer components, different components, or components arranged differently than those shown in Figure 2. Additionally or alternatively, a set of components (e.g., one or more components) of device 200 may perform one or more functions described as being performed by other sets of components of device 200.

[0076] Reference is now made to Figure 3, which is a flow chart of a non-limiting embodiment or aspect of a process 300 for predictive maintenance of an injection system. In some non-limiting embodiments or aspects, one or more of the steps of the process 300 are performed (e.g., completely, partially, etc.) by the maintenance prediction system 102 (e.g., one or more devices of the maintenance prediction system 102, etc.). In some non-limiting embodiments or aspects, one or more of the steps of the process 300 are performed (e.g., completely, partially, etc.) by another device or group of devices separate from or including the maintenance prediction system 102, such as the injection system 104 (e.g., one or more devices of the injection system 104, etc.) and / or the remote system 106 (e.g., one or more devices of the remote system 106, etc.).

[0077] 3, the process 300 includes receiving operational data associated with one or more injection systems at step 302. For example, the maintenance prediction system 102 receives operational data associated with the injection system 104. As an example, the maintenance prediction system 102 can receive operational data associated with the infusion system 104 from the infusion system 104, the remote system 106, and / or one or more other devices associated with a surgery, a medical procedure, a patient, and / or a user or operator associated with the infusion system 104 (e.g., a power supply system, an imaging device or scanner such as a computed tomography (CT) system, a magnetic resonance imaging (MRI) system, and / or the like, a patient device such as a patient identification device (e.g., a wearable RFID tag and / or a computing device) including a patient identifier and / or patient information, a heart rate monitor, and / or the like, a user or operator device such as a user or operator identification device (e.g., a wearable RFID tag and / or a computing device) including a user or operator identifier and / or user or operator information, and / or the like). In such examples, the infusion system 104, the remote system 106, and / or one or more other medical devices may include one or more sensors, one or more input components, one or more output components, and / or the like that can receive, determine, measure, sense, and / or provide operational data associated with a function, a medical procedure, a patient, and / or a user or operator associated with the infusion system 104 (e.g., a function, a medical procedure, a patient, and / or a user or operator associated with one or more operations of the infusion system 104). In some non-limiting embodiments or aspects, the maintenance prediction system 102 receives operational data associated with the infusion system 104 continuously, periodically in response to a user request for predictive maintenance analysis, diagnostic actions, and / or benchmark inspections to be performed in association with the infusion system 104, automatically in response to start-up actions and / or other actions performed by the infusion system 104, and / or the like.

[0078] In some non-limiting embodiments or aspects, the operational data includes one or more operational parameters associated with one or more operations of the one or more infusion systems. For example, the operational data may include one or more operational parameters associated with one or more operations of the infusion system 104. By way of example, the operations of the infusion system may include the following operations or functions: software operations or functions (e.g., receiving, installing, modifying, updating, initiating, executing, and / or monitoring one or more software applications, etc.), hardware operations or functions (e.g., receiving, installing, modifying, updating, powering, operating, and / or monitoring one or more hardware components or devices, such as a microprocessor, memory, storage components, input components, output components, circuit boards, sensors, pumps, valves, and / or the like), mechanical operations or functions (e.g., flow The operational parameters may include at least one of: receiving, providing, altering, and / or monitoring a body flow, and / or delivering a fluid to a medical device and / or a patient; electrical operations or functions (e.g., receiving, providing, consuming, and / or monitoring electrical power); user interface operations or functions (e.g., providing a user interface via the output components 212, receiving user input via the input components 210, etc.); communication operations or functions (e.g., receiving, altering, updating, storing, providing, and / or monitoring operational and / or maintenance data, etc.); or any combination thereof. In such examples, the operational parameters may include parameters received, determined, measured, sensed, and / or provided in association with a function, a medical procedure, a patient, and / or a user or operator associated with the infusion system 104 (e.g., a function, a medical procedure, a patient, and / or a user or operator associated with one or more operations of the infusion system 104, etc.).

[0079] In certain non-limiting embodiments or aspects, the operating parameters include parameters associated with the injection system (e.g., at least one of the following parameters associated with one or more components or devices of the injection system): flow rate during one or more injections (e.g., maximum, minimum, average, total, etc.), flow rate programmed to be achieved over one or more injections, amount pumped and / or delivered during one or more injections (e.g., maximum, minimum, average, total, etc.), amount programmed to be delivered during one or more injections, duration of one or more injections (e.g., maximum, minimum, average, total, etc.), difference between the flow rate during one or more injections and the programmed flow rate (e.g., as set by an injection parameter of an injection protocol, etc.) of one or more injections, difference between the amount pumped and / or delivered during one or more injections and the amount programmed to be pumped and / or delivered (e.g., as set by an injection parameter of an injection protocol, etc.) during one or more injections, number of injections performed, achieved pressure (e.g., maximum, minimum, average, total, etc.), achieved pressure of one or more injections and the amount programmed to be pumped and / or delivered during one or more injections (e.g., as set by an injection parameter of an injection protocol, etc.), the difference between the pressure programmed to be achieved during the injection (e.g., as set by the injection parameters of the injection protocol, etc.), the pressure limit or threshold above which the injection system is programmed to stop delivering the injection, the duration of power on (maximum, minimum, average, total, etc.), the number of power cycles, the amount of energy consumed (e.g., maximum, minimum, average, total, etc.), the amount of linear power supplied or used (e.g., the integral of ((pressure)*(flow rate)) / (time), etc.), the amount of non-linear power supplied or used (e.g., the integral of f(pressure)*(time)), voltage (e.g., maximum, minimum, average, total, etc.), resistance experienced by a motor of the injection system, current, noise or signal levels, mechanical force, and / or the like (e.g., maximum, minimum, average, total, etc.), number of camera reads, presence or operability of communications with one or more other systems or devices, number and / or type of error codes received, number, duration, and / or type of user interface keys actuated (e.g., pressed, etc.), power line condition, temperature and / or humidity within the injection system (e.g., maximum, minimum, average, total, etc.),temperature and / or humidity (e.g. maximum, minimum, average, total, etc.) of the environment surrounding the injection system, vibration frequency and / or amplitude (e.g. maximum, minimum, average, total, etc.), movements above a threshold movement (e.g. as measured by an accelerometer, etc.), number of cleanings, staff rating of wear (e.g. numerical rating, etc.), staff rating of cleanliness (e.g. numerical rating, etc.), inspection records of inspections (e.g. number of inspections performed, type of inspections performed, etc.), system rating of wear (e.g. numerical rating, etc.), system rating of cleanliness (e.g. numerical rating, etc.), number of disposable items (e.g. syringes, transfer sets, etc.) sold to and / or used by the relevant customer, contrast agent used the amount of contrast agent used, the type of contrast agent used, the vial size of the contrast agent used, the number of injection systems at an imaging site that includes the injection system, a user or operator turnover rate associated with the imaging site that includes the injection system, a user or operator identifier associated with one or more operations or uses of the injection system, a customer identifier associated with the imaging site that includes the injection system, an indication of liquid in the injection system (e.g., as detected or measured by one or more liquid sensors, etc.), an amount (e.g., maximum, minimum, average, total, etc.) of x-ray radiation, RF exposure, magnetic field exposure, and / or the like in the environment surrounding the injection system, one or more injection protocols used for one or more injections, and / or the like.

[0080] In some non-limiting embodiments or aspects, operational data associated with the infusion system 104 may be stored in a computer-readable form as described in U.S. Patent Application No. 10 / 143,562, filed May 10, 2002, which issued as U.S. Patent No. 7,457,804; U.S. Patent Application No. 12 / 254,318, filed October 20, 2008, which issued as U.S. Patent No. 7,996,381; U.S. Patent Application No. 13 / 180,175, filed July 11, 2011, which issued as U.S. Patent No. 8,521,716; U.S. Patent Application No. 13 / 180,175, filed August 11, 2016, which issued as U.S. Patent No. 8,521,716; The present invention may include one or more exemplary data types, information types, and / or parameters disclosed in International Patent Application Publication No. WO2017 / 027724A1, filed as International Application No. PCT / US2016 / 046587, published on February 16, 2017, and International Patent Application Publication No. WO2017 / 040152A1, filed as International Application No. PCT / US2016 / 048441, published on March 9, 2017, the disclosures of each of which are incorporated herein by reference in their entireties.

[0081] 3, at step 304, the process 300 includes determining one or more predictive scores for the one or more injection systems based on the operational data. For example, the maintenance prediction system 102 determines one or more predictive scores for the injection system 104 based on the operational data. As an example, the maintenance prediction system 102 determines the one or more predictive scores for the injection system 104 based on the operational data on an ongoing basis, periodically in response to a user request for predictive maintenance analysis, diagnostic actions, and / or benchmark inspections to be performed in association with the injection system 104, automatically in response to start-up actions and / or other actions performed by the injection system 104, and / or the like.

[0082] In some non-limiting embodiments or aspects, the one or more predictive scores include one or more predictions of one or more operational faults or misuses for the infusion system 104. For example, the predictive scores include an indication (e.g., a score, a number, a ranking, a probability, a likelihood, etc.) of the fault or misuse occurring with respect to the infusion system 104. By way of example, the operational fault or misuse may include a fault or misuse of the infusion system 104 (e.g., a fault or misuse of one or more operations of the infusion system 104, a fault or misuse of one or more devices of the infusion system 104 and / or one or more components of the one or more devices, etc.). In such examples, the operational fault of the infusion system 104 may include at least one of a software fault, a hardware fault, a component or device fault, and / or the like that causes the infusion system 104 to operate contrary to one or more predetermined operational thresholds. For example, the operational fault may require service, repair, and / or replacement of the software, hardware, component, or device, and / or the like that is affected by the operational fault in order for the infusion system 104 to operate in a proper manner. In such examples, misuse of the injection system 104 may include at least one of inputs to the injection system 104, settings of the injection system 104, operations of the injection system 104, and / or the like by a user or operator of the injection system 104 that cause the injection system 104 to operate contrary to one or more predetermined operational thresholds.

[0083] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more prediction scores based on one or more machine learning techniques (e.g., pattern recognition techniques, data mining techniques, heuristic techniques, supervised learning techniques, unsupervised learning techniques, etc.). For example, the maintenance prediction system 102 generates one or more models (e.g., estimators, classifiers, predictive models, operational failure or misuse predictive models, maintenance prediction models, etc.) based on one or more machine learning algorithms (e.g., decision tree algorithms, gradient boosted decision tree algorithms, neural network algorithms, convolutional neural network algorithms, random forest algorithms, etc.). In such examples, the maintenance prediction system 102 generates one or more prediction scores using the one or more models.

[0084] In some non-limiting embodiments or aspects, the one or more predictive models are designed to receive as input operational data associated with the infusion system 104 and provide as output a prediction (e.g., a probability, a binary output, a percentage, a yes-no output, a score, a prediction score, etc.) regarding one or more operational failures or misuses of the infusion system 104. For example, the maintenance prediction system 102 generates one or more predictive models for predicting one or more operational failures or misuses of the one or more infusion systems. As an example, the maintenance prediction system 102 can generate one or more predictive models to determine one or more predictive scores that include a prediction of whether one or more operational failures or misuses of the one or more infusion systems (e.g., one or more components or devices of the one or more infusion systems) will occur within a predetermined time period and / or within a predetermined number of uses of the one or more infusion systems.

[0085] In some non-limiting embodiments or aspects, the maintenance forecasting system 102 stores one or more predictive models (e.g., stores one or more models for later use). In some non-limiting embodiments or aspects, the maintenance forecasting system 102 stores one or more predictive models in a data structure (e.g., a database, a linked list, a tree, etc.). In some non-limiting embodiments or aspects, the data structure is located within the maintenance forecasting system 102 or external to the maintenance forecasting system 102 (e.g., at a location remote from the maintenance forecasting system 102).

[0086] In some non-limiting embodiments or aspects, the maintenance prediction system 102 processes operational data (e.g., operational data associated with the injection system 104, operational data associated with multiple injection systems 104, etc.) to obtain training data for one or more models. For example, the maintenance prediction system 102 processes the operational data to modify the operational data into a format that can be analyzed (e.g., by the maintenance prediction system 102) to generate one or more models. The modified operational data is referred to as training data. In some implementations, the maintenance prediction system 102 processes the operational data to obtain training data based on receiving the operational data. Additionally or alternatively, the maintenance prediction system 102 processes the operational data to obtain training data based on the system 102 receiving an instruction from its user that the system 102 should process the operational data, such as, for example, when the maintenance prediction system 102 receives an instruction to create a model.

[0087] In some non-limiting embodiments or aspects, the maintenance prediction system 102 processes the operational data by determining one or more variables based on the operational data. In some non-limiting embodiments or aspects, the variables include indicators related to operational faults or misuse of the injection system 104 that may be derived based on the operational data. The variables are analyzed to generate a model. For example, the variables include variables associated with one or more operational parameters of the injection system 104.

[0088] In some non-limiting embodiments or aspects, the maintenance prediction system 102 analyzes the training data to generate a model (e.g., one or more predictive models). For example, the maintenance prediction system 102 uses machine learning techniques to analyze the training data and generate the model. In some implementations, generating the model (e.g., based on training data obtained from operational data, based on training data obtained from existing operational data, etc.) is referred to as training the model. Machine learning techniques include supervised and / or unsupervised techniques, such as, for example, decision trees (e.g., gradient boosted decision trees, etc.), logistic regression, artificial neural networks (e.g., convolutional neural networks, etc.), Bayesian statistics, learning automata, hidden Markov modeling, linear classifiers, quadratic classifiers, association rule learning, random forests, and / or the like. In some non-limiting embodiments or aspects, the model includes a predictive model specific to a particular infusion system 104, a particular number of infusion systems 104, a particular operating parameter, a particular number of operating parameters, a particular operational fault and / or misuse, a particular number of operational faults and / or misuse, and / or the like. Additionally or alternatively, the predictive model may be specific to a particular user or operator (e.g., a particular user or operator of the injection system 104, a particular imaging site that includes the injection system 104, a particular customer that operates the injection system 104, etc.).

[0089] Additionally or alternatively, when analyzing the training data, the maintenance prediction system 102 identifies one or more variables (e.g., one or more independent variables) as predictor variables that are used to make the predictions (e.g., when analyzing the training data). In some implementations, the values ​​of the predictor variables are inputs to the model. For example, the maintenance prediction system 102 identifies a subset (e.g., a suitable subset) of the variables as predictor variables that are used to accurately predict one or more operational faults or misuses for the infusion system. In some implementations, the predictor variables include one or more variables (e.g., one or more of the operational parameters, etc.) as described above that significantly affect the probability that an operational fault or misuse will occur for the infusion system within a given time period or within a given number of uses of the infusion system.

[0090] In some non-limiting embodiments or aspects, the maintenance forecasting system 102 validates the model. For example, the maintenance forecasting system 102 validates the model after the maintenance forecasting system 102 generates the model. In some implementations, the maintenance forecasting system 102 validates the model based on a portion of the training data to be used for validation. For example, the maintenance forecasting system 102 splits the training data into a first portion and a second portion, where the first portion is used to generate the model as described above. In this example, the second portion of the training data (e.g., validation data, etc.) is used to validate the model. In some non-limiting embodiments or aspects, the first portion of the training data is different from the second portion of the training data.

[0091] In some non-limiting embodiments or aspects, the maintenance prediction system 102 validates the model by providing validation data including operational data including a plurality of operational parameters of the plurality of infusion systems as input to the model and determining whether the predictive model accurately or inaccurately predicted an operational fault or misuse of one or more of the plurality of infusion systems based on the output of the predictive model. In some implementations, the maintenance prediction system 102 validates the model based on a validation threshold (e.g., a threshold of validation data). For example, the maintenance prediction system 102 is configured to validate the model when an operational fault or misuse of the infusion system (e.g., an operational fault or misuse within a predetermined time period or within a predetermined number of uses of the infusion system, etc.) is accurately predicted by the model (e.g., when the predictive model accurately predicts 50% of the validation data, when the predictive model accurately predicts 70% of the validation data, etc.). In some non-limiting embodiments or aspects, if the maintenance prediction system 102 does not validate the model (e.g., when a percentage of the validation data does not meet the validation threshold), the maintenance prediction system 102 generates an additional predictive model.

[0092] In some non-limiting embodiments or aspects, if one or more models have been validated, the maintenance prediction system 102 further trains the one or more models and / or creates new models based on receiving new training data. In some non-limiting embodiments or aspects, the new training data includes operational data associated with multiple injection systems 104 that are different from the previous multiple injection systems 104 already used to train the one or more models.

[0093] In some non-limiting embodiments or aspects, predictions from multiple machine learning algorithms may be combined to build an ensemble of algorithms or predictive models to increase predictive power. In such an example, the maintenance forecasting system 102 selects a machine learning algorithm or model from multiple machine learning algorithms or models based on the ability of the algorithm or model to predict the training data. For example, the maintenance forecasting system 102 may compare the accuracy of various models in predicting the training data. In such an example, the maintenance forecasting system 102 may build an ensemble model by averaging predictions of selected models, comparing a plot with the sum of squared residuals to the individual models, and selecting the ensemble model or the individual models based on the comparison.

[0094] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more predictive scores based on one or more measurements of flow rates and volumes delivered by the injection system 104. As an example, the maintenance prediction system 102 can receive operational data including measurements of flow rates of one or more injections using the injection system 104 (e.g., measurements independent of pump type, including non-positive displacement pumps) and measurements of total volumes delivered by one or more injections using the injection system 104 (e.g., measurements from the flow sensor 124 of the injection system 104 used in a feedback control loop between the flow sensor 124 and the pump 120), and apply one or more models and / or one or more equations or expressions (e.g., cube root life equations, etc.) to the operational data to determine a predictive score for a prediction of remaining time and / or remaining number of uses of the injection system 104 until operational failure or misuse. In such an example, the maintenance prediction system 102 can apply a cube root life equation that can replace the average load with the maximum achieved pressure, and can use the injection volume and flow rate to establish the duration of the load to determine a predictive score.

[0095] In some non-limiting embodiments or aspects, the maintenance prediction system 102 uses a pattern prediction model to determine one or more predictive scores. For example, the maintenance prediction system 102 can receive operational data including operational parameters associated with objective causes of infusion system wear (e.g., one or more temperatures over one or more time periods within the infusion system 104 and / or within the environment surrounding the infusion system 104, one or more vibrations over one or more time periods within the infusion system 104, etc.) and / or operational parameters associated with subjective causes of infusion system wear (e.g., ratings associated with staff handling of the infusion system 104, etc.), input the operational data into the pattern prediction model, and receive as an output a predictive score for a prediction of remaining time and / or remaining number of uses of the infusion system 104 until operational failure or misuse. As an example, the maintenance prediction system 102 can alert hospital management when a non-random pattern is recognized in the inspection history of the infusion system 104. In such an example, the pattern recognition system can be cross-linked to hospital staff records to identify the user or operator (e.g., hospital staff, etc.) on duty when a problem is reported. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the prediction score in an attempt to mitigate predicted operational failures or misuse.

[0096] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more predictive scores for the injection system 104 based on one or more comparisons of the operational data associated with the injection system 104 to the operational data associated with a plurality of other injection systems. For example, the maintenance prediction system may aggregate operational data from a plurality of injection systems 104 (e.g., from injection systems at the same location, from injection systems at the same imaging site, from injection systems at different remote locations, from injection systems at different imaging sites, etc.) and compare the operational data of one or more of the injection systems 104 to the operational data of one or more other injection systems 104. As an example, the maintenance prediction system 102 may provide as a predictive score a comparison (e.g., a numerical difference, etc.) between the operational data (e.g., inspection records, the number of times the injection system 104 has been inspected, etc.) of the one or more injection systems (e.g., an injection system at an imaging site operated by a customer, etc.) and the operational data of one or more other injection systems (e.g., a global database of injection systems at imaging sites operated by one or more other customers, etc.). In such examples, the maintenance prediction system 102 can determine whether one or more infusion systems of a customer are experiencing a substantially different number of operational failures or misuses (e.g., meeting a threshold difference, etc.) as compared to the number of operational failures or misuses experienced by one or more other infusion systems (e.g., for other customers and / or other imaging sites for the same customer, etc.). For example, the maintenance prediction system 102 can use inspection benchmarking to indicate how the infusion system 104 associated with the customer or hospital compares to infusion systems associated with other customers or hospitals (e.g., how the operation and / or use of the infusion system 104 compares to other infusion systems, etc.). In such examples, the maintenance prediction system 102 can determine cost savings associated with performing one or more maintenance actions performed for and / or with the infusion system 104.For example, the maintenance prediction system 102 can determine cost savings as the cost of the amount of contrast agent saved by reducing contrast agent waste by performing one or more maintenance actions compared to one or more other injection systems.

[0097] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more predictive scores based on the energy consumption of one or more injection systems. For example, the maintenance prediction system 102 can apply, as an input, the energy consumption of the injection system 104 to one or more predictive models and / or one or more lookup tables, equations, and / or thresholds, and receive, as an output, a predictive score for a prediction of the remaining time of use and / or remaining number of uses of the injection system 104 until operational failure or misuse. For example, the maintenance prediction system 102 can automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the predictive score in an attempt to mitigate the predicted operational failure or misuse. As an example, the maintenance prediction system 102 can automatically schedule an inspection technician (e.g., dispatch an inspection technician to the injection system 104) in response to the energy consumption of the injection system 104 meeting a threshold indicating that the injection system 104 has reached the end of its expected useful life and / or that an imminent failure of the injection system 104 is expected.

[0098] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more predictive scores based on customer-specific usage of one or more infusion systems. For example, the maintenance prediction system 102 can receive usage-based operating parameters (e.g., a number of one or more disposables (e.g., syringes, transfer sets, etc.) used in association with the infusion system 104, a quantity, type, vial size of contrast used in association with the infusion system 104, and / or the like), local regulations and / or practices associated with the infusion system 104 indicating thresholds for one or more usage-based operating parameters, an indication of lack of contrast warming, a number of infusion systems associated with the customer, a turnover rate of users or operators of the infusion system 104, a performance rating of users or operators associated with the infusion system 104, and / or the like. For example, the maintenance prediction system 102 can apply as input the one or more usage-based operating parameters to one or more predictive models and / or one or more lookup tables, equations, and / or thresholds, and receive as output a predictive score for a prediction of remaining time of use and / or remaining number of uses of the infusion system 104 until operational failure or misuse. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can determine a tailored preventive maintenance schedule for a customer based on the predicted reliability of the injection system 104.

[0099] In some non-limiting embodiments or aspects, the maintenance prediction system determines one or more predictive scores based on a contrast agent fouling scoring system. For example, the maintenance prediction system 102 can receive one or more assessments related to the cleanliness of the injection system 104.

[0100] In some non-limiting embodiments or aspects, the injection system 104 can provide a prompt (e.g., via a user interface with output component 212, etc.) requesting a user or operator of the injection system 104 to input (e.g., via a user interface associated with input component 210) an assessment of the cleanliness of the injection system 104 (e.g., on a numerical scale, etc.). In such examples, the injection system 104 can provide the prompt before, during, and / or after each operation and / or injection process by the injection system 104, at regular intervals, in response to a user request to assess cleanliness, and / or the like. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can apply as input one or more cleanliness assessments to one or more predictive models and / or one or more lookup tables, equations, and / or thresholds, and receive as output a predictive score for a prediction of the remaining time and / or number of uses of the injection system 104 until operational failure or misuse. For example, the maintenance prediction system 102 can automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the prediction score in an attempt to mitigate the predicted operational failure or misuse. As an example, the maintenance prediction system 102 can automatically schedule an inspection technician (e.g., dispatch an inspection technician to the injection system 104) in response to the total cleanliness rating of the injection system 104 meeting a threshold. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can associate the predicted scores of multiple injection systems 104 determined based on the cleanliness ratings with an identifier for the user or operator operating the injection system to compare the users or operators to one another with respect to the cleanliness ratings of their systems.

[0101] In some non-limiting embodiments or aspects, the injection system 104 can determine a cleanliness rating (e.g., on a numerical scale, etc.) of the injection system 104. For example, the injection system 104 can determine a cleanliness rating of the injection system 104 based on one or more scans of at least one barcode (BC) by the optical sensor 158 and / or image capture device 140, one or more images of the field of view of the image capture device 140, one or more force measurements taken by the force sensor 160, or any combination thereof. In such examples, the injection system 104 can capture scans, images, and / or force measurements before, during, and / or after each operation and / or injection process by the injection system 104, at regular intervals, in response to a user request to assess cleanliness, and / or the like. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can apply, as input, one or more cleanliness ratings to one or more predictive models and / or one or more lookup tables, equations, and / or thresholds, and receive, as output, a predictive score for predicting the remaining time of use and / or remaining number of uses of the injection system 104 until operational failure or misuse. For example, the maintenance prediction system 102 can automatically recalibrate and / or adjust the duty cycle or other operation of the injection system 104 based on the predictive score in an attempt to mitigate the predicted operational failure or misuse. As an example, the maintenance prediction system 102 can automatically schedule an inspection technician (e.g., dispatch an inspection technician to the injection system 104) in response to an overall cleanliness rating of the injection system 104 meeting a threshold. In some non-limiting embodiments or aspects, the maintenance prediction system 102 can associate the predictive scores of multiple injection systems 104 determined based on the cleanliness ratings with an identifier for the user or operator operating the injection system to compare the users or operators to one another with respect to the cleanliness ratings of those systems.

[0102] 3, in step 306, the process 300 includes providing maintenance data associated with the one or more operational faults or misuse. For example, the maintenance prediction system 102 provides maintenance data associated with the one or more operational faults or misuse. As an example, the maintenance prediction system 102 provides maintenance data related to one or more operational faults or misuses (e.g., via a user interface provided by an output component 212 of the maintenance prediction system 102, via a user interface provided by an output component 212 of the injection system 104, etc.), to a user or operator of the injection system 104, to the injection system 104, to a remote system 106 (e.g., to a computer system implemented by or on behalf of an original equipment manufacturer OEM of the injection system 104 (e.g., an OEM of one or more components or devices of the injection system 104), to a computer system implemented by or on behalf of a provider of the injection system 104 (e.g., the MEDRAD® Stellant CT Injection System with Certegra® Workstation provided by Bayer, etc.), to an imaging site, customer, or hospital), and / or the like.

[0103] In some non-limiting embodiments or aspects, the maintenance prediction system 102 provides the maintenance data to a user or operator, an infusion system, and / or a remote computer system or entity based on an identifier of the user or operator, an identifier of the infusion system, and / or an identifier of the remote computer system or entity included in the maintenance data and / or operational data used to determine the one or more predictive scores on which the maintenance data is based. In some non-limiting embodiments or aspects, the type and / or amount of maintenance data provided is based on the recipient of the maintenance data.

[0104] In some non-limiting embodiments or aspects, the maintenance data includes operational data (e.g., one or more operating parameters associated with one or more operations of the injection system 104, etc.) and / or data associated with one or more maintenance actions (e.g., a prompt prompting a user or operator to perform one or more maintenance actions, instructions to cause the injection system 104 to perform one or more maintenance actions, an indication that one or more maintenance actions are scheduled to be performed for and / or with the injection system 104, an indication that one or more maintenance actions have been performed for and / or with the injection system 104, a list of other injection systems among multiple injection systems at the imaging site that includes the injection system 104, one or more maintenance contracts associated with the injection system 104, etc.).

[0105] In some non-limiting embodiments or aspects, the maintenance data is based on one or more predictive scores. For example, the maintenance prediction system 102 can determine the maintenance data based on the one or more predictive scores. As an example, the maintenance prediction system 102 can query a lookup table or database that associates one or more maintenance actions with one or more anticipated operational malfunctions or misuses of the infusion system 104 (e.g., one or more anticipated operational malfunctions or misuses of one or more components or devices of the infusion system 104, etc.) based on the one or more predictive scores. In such an example, the maintenance prediction system 102 can retrieve and / or provide maintenance data related to one or more maintenance actions related to one or more operational malfunctions or misuses of the infusion system 104 that have one or more predictive scores that meet one or more threshold scores.

[0106] In some non-limiting embodiments or aspects, the maintenance data includes instructions to automatically perform one or more maintenance actions on the injection system 104. For example, the maintenance actions may include the following actions that are automatically performed with the injection system 104 (e.g., one or more components or devices of the injection system 104): providing a prompt (e.g., via a user interface of the output component 212, etc.) to a user to perform one or more maintenance actions for the injection system 104, scheduling a service technician (e.g., dispatching a service technician to the injection system 104, etc.) to repair, service, and / or replace the injection system 104, automatically ordering one or more disposable items (e.g., syringes, transfer sets, etc.) and / or one or more contrast media, providing instructions (e.g., via a user interface of the output component 212, etc.) to a user or operator to use the injection system 104 in a particular manner to avoid certain operational failures and / or misuse of the injection system 104, and providing recommendations (e.g., via a user interface of the output component 212, etc.) for improving service based on a comparison of the injection system 104 to one or more other injection systems. providing (e.g., via a user interface of the output component 212, etc.) a maintenance plan based on operating parameters based on usage of the injection system 104 (e.g., via a user interface of the output component 212, etc.); providing (e.g., via a user interface of the output component 212, etc.) a customized preventive maintenance check (e.g., cleaning, calibrating power supplies, motors, etc.) (e.g., via a user interface of the output component 212, etc.); recommending training to a user or operator (e.g., via a user interface of the output component 212, etc.); rebooting software, updating software, transmitting operating data and / or alerts to the remote system 106, measuring component degradation, wear, or cleanliness, providing remote input to a remote computer system to change and / or update the software and / or one or more operating parameters, disabling or limiting one or more operating parameters or functions (e.g.,The method may include at least one of disabling the injection due to operational parameters defining a flow rate that meets a threshold flow rate and / or a pressure that meets a threshold pressure, disabling power, stopping the injection, power cycling, prompting the customer to send a service request directly from the injection system 104, and / or the like.

[0107] In some non-limiting embodiments or aspects, the maintenance prediction system 102 determines one or more predictive scores for the infusion system 104 and / or provides maintenance data related to the one or more predictive scores continuously, periodically, or automatically in response to startup actions and / or other actions performed by the infusion system 104.

[0108] While the present invention has been described in detail for purposes of illustration based on what are presently considered to be the most practical and preferred embodiments or aspects, it should be understood that such details are for this purpose only and that the invention is not limited to the disclosed embodiments or aspects, but on the contrary, the invention is intended to cover modifications and equivalent arrangements falling within the spirit and scope of the appended claims. For example, it should be understood that the present invention contemplates that one or more features of any embodiment or aspect can be combined, to the extent possible, with one or more features of any other embodiment or aspect. [Explanation of symbols]

[0109] 100 Environment 102 Maintenance Prediction System 104 Injection System 106 Remote Systems 108 Network 120 Injectors, pumps 122 Flow Tube 124 Flow Sensor 126 Air Sensor 128 Medical Devices 140 Image Capture Device 150 Housing 152 Syringe Port 154 Syringes, Syringe Ports 156 Plunger 158 Optical Sensor 160 Force Sensor 200 devices 202 Bus 204 Processor 206 Memory 208 Memory Components 210 Input Component 212 Output Components 214 Communication Interface

Claims

1. 1. A computer-implemented method for predictive maintenance, comprising: receiving, by a computer system having one or more processors, operational data associated with one or more infusion systems, the operational data including one or more operational parameters associated with one or more operations of the one or more infusion systems; determining, by the computer system, one or more predictive scores for the one or more infusion systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational disorders or misuse for the one or more infusion systems; providing, by the computer system, maintenance data related to the one or more motion disorders or misuses, the maintenance data being based on the one or more predictive scores; The method includes:

2. The computer-implemented method of claim 1 , wherein the maintenance data includes a prompt to a user to initiate at least one maintenance action associated with the one or more infusion systems.

3. 3. The computer-implemented method of claim 2, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

4. 10. The computer-implemented method of claim 1, wherein the maintenance data comprises instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

5. 5. The computer-implemented method of claim 4, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, performing a particular operation by the one or more injection systems, or any combination thereof.

6. The one or more operating parameters include a cleanliness rating related to a cleanliness of the one or more injection systems, and the method further comprises: determining, by the computer system, the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof; 2. The computer implemented method of claim 1, further comprising:

7. 2. The computer-implemented method of claim 1, wherein the one or more operational failures or misuses of the one or more infusion systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more infusion systems a user input from a user of the one or more infusion systems that causes the one or more infusion systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

8. 1. A predictive maintenance system comprising: a computer system having one or more processors; the one or more processors are programmed or configured to receive operational data associated with one or more infusion systems, the operational data including one or more operational parameters associated with one or more operations of the one or more infusion systems; the one or more processors are programmed or configured to determine one or more predictive scores for the one or more infusion systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational failures or misuses for the one or more infusion systems; The one or more processors are programmed or configured to provide maintenance data related to the one or more operational faults or misuses, the maintenance data being based on the one or more predictive scores.

9. 10. The system of claim 8, wherein the maintenance data includes a prompt to prompt a user to initiate at least one maintenance action associated with the one or more infusion systems.

10. 10. The system of claim 9, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

11. The system of claim 8 , wherein the maintenance data includes instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

12. 12. The system of claim 11, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, performing a particular operation by the one or more injection systems, or any combination thereof.

13. The one or more operating parameters include a cleanliness rating related to a cleanliness of the one or more injection systems, the one or more processors: determining the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof; The system of claim 8, further programmed or configured to:

14. 10. The system of claim 8, wherein the one or more operational failures or misuses of the one or more infusion systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more infusion systems a user input from a user of the one or more infusion systems that causes the one or more infusion systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

15. 1. A computer program product for predictive maintenance, the computer program product comprising at least one non-transitory computer readable medium comprising one or more instructions, which when executed by at least one processor, cause the at least one processor to: receiving operational data associated with one or more infusion systems, the operational data including one or more operational parameters associated with one or more operations of the one or more infusion systems; The instructions cause the at least one processor to determine one or more predictive scores for the one or more infusion systems based on the operational data, the one or more predictive scores including one or more predictions of one or more operational failures or misuse for the one or more infusion systems; the instructions cause the at least one processor to provide maintenance data related to the one or more performance disorders or misuses, the maintenance data being based on the one or more predictive scores; Computer program products.

16. 16. The computer program product of claim 15, wherein the maintenance data includes a prompt to prompt a user to initiate at least one maintenance action associated with the one or more infusion systems.

17. 17. The computer program product of claim 16, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, operating the one or more injection systems in a particular manner indicated by the maintenance data, or any combination thereof.

18. 16. The computer program product of claim 15, wherein the maintenance data comprises instructions to the one or more injection systems that cause the one or more injection systems to automatically initiate at least one maintenance action.

19. 20. The computer program product of claim 18, wherein the at least one maintenance action includes at least one of: scheduling an inspection for the one or more injection systems, performing a particular operation by the one or more injection systems, or any combination thereof.

20. The one or more operating parameters include a cleanliness rating related to a cleanliness of the one or more injection systems, the instructions further comprising: determining the cleanliness rating based on at least one of: one or more images of the one or more injection systems, one or more force measurements of an injector motor of the one or more injection systems, or any combination thereof; 16. A computer program product according to claim 15.

21. 16. The computer program product of claim 15, wherein the one or more operational failures or misuses of the one or more infusion systems include at least one of an electrical component failure, a software component failure, a mechanical component failure, receiving by the one or more infusion systems a user input from a user of the one or more infusion systems that causes the one or more infusion systems to operate contrary to one or more predetermined operational thresholds, or any combination thereof.

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